# coding:utf-8
# Author : hiicy redldw
# Date : 2019/06/29
"""
高维特征 内容
低维特征，特征间关系~gram 再比较差异

测量两个图像之间的内容有多不同，而 测量两个图像之间样式的差异。
然后，我们获取第三个图像输入，并将其转换为最小化与内容图像的内容距离和与样式图像的样式距离
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
import copy

plt.switch_backend('agg')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# desired size of the output image
imsize = 512 if torch.cuda.is_available() else 128

loader = transforms.Compose([
    transforms.Resize(imsize),  # scale imported image
    transforms.ToTensor()])


def image_loader(image_name):
    image = Image.open(image_name)
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)


style_img = image_loader(r"F:\temp\picasso.jpg")
content_img = image_loader(r'F:\temp\dancing.jpg')
print(f'content_img: {content_img.shape}')
assert style_img.size() == content_img.size(), \
    "we need to import style and content images of the same size"

unloader = transforms.ToPILImage()  # reconvert into PIL image

plt.ion()


def imshow(tensor, title=None):
    image = tensor.cpu().clone()  # we clone the tensor to not do changes on it
    image = image.squeeze(0)  # remove the fake batch dimension
    image = unloader(image)
    plt.imshow(image)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


# plt.figure()
imshow(style_img, title='Style Image')

# plt.figure()
# imshow(content_img, title='Content Image')
class ContentLoss(nn.Module):
    def __init__(self, target):
        # 内容损失都会在目标层被计算。而且因为自动求导的缘故，所有的梯度都会被计算
        super(ContentLoss, self).__init__()
        self.target = target.detach()

    def forward(self, input):
        # 计算的损失作为模型的参数被保存
        # print('input',input.shape,)
        # print('target',self.target.shape)
        self.loss = F.mse_loss(input, self.target)
        return input


def gram_matrix(input):
    a, b, c, d = input.size()

    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    # we 'normalize' the values of the gram matrix
    return G.div(a * b * c * d)


class StyleLoss(nn.Module):
    def __init__(self, target_feature):
        super(StyleLoss, self).__init__()
        self.target = gram_matrix(target_feature).detach()

    def forward(self, input):
        G = gram_matrix(input)
        self.loss = F.mse_loss(G, self.target)
        return input


cnn = models.vgg19(pretrained=True).features.to(device).eval()

cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)


# create a module to normalize input image so we can easily put it in a
# nn.Sequential
class Normalization(nn.Module):
    def __init__(self, mean, std):
        super(Normalization, self).__init__()
        # .view the mean and std to make them [C x 1 x 1] so that they can
        # directly work with image Tensor of shape [B x C x H x W].
        # B is batch size. C is number of channels. H is height and W is width.
        self.mean = torch.tensor(mean).view(-1, 1, 1)
        self.std = std.clone().detach().view(-1, 1, 1)

    def forward(self, img):
        # normalize img
        return (img - self.mean) / self.std


content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']


# print(cnn)


def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
                               style_img, content_img, content_layers=content_layers_default,
                               style_layers=style_layers_default):
    cnn: models.vgg19 = copy.deepcopy(cnn)
    #
    normalization = Normalization(normalization_mean, normalization_std).to(device)
    # just in order to have an iterable access to or list of content/style losses
    content_losses = []
    style_losses = []

    model = nn.Sequential(normalization)
    i = 0

    for layer in cnn.children():
        if isinstance(layer, nn.Conv2d):
            i += 1
            name = 'conv_{}'.format(i)
        elif isinstance(layer, nn.ReLU):
            name = 'relu_{}'.format(i)
            layer = nn.ReLU(inplace=False)
        elif isinstance(layer, nn.MaxPool2d):
            name = 'pool_{}'.format(i)
        elif isinstance(layer, nn.BatchNorm2d):
            name = 'bn_{}'.format(i)
        else:
            raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))

        model.add_module(name, layer)

        if name in content_layers:
            # REW:每一层都加损失层
            target = model(content_img).detach()
            content_loss = ContentLoss(target)
            # REW:内容损失层在高层卷积(提取内容特征)后添加，然后计算损失
            model.add_module('content_loss_{}'.format(i), content_loss)
            content_losses.append(content_loss)

        if name in style_layers:
            target_feature = model(style_img).detach()
            style_loss = StyleLoss(target_feature)
            # 样式损失每个卷积都加
            model.add_module('style_loss_{}'.format(i), style_loss)
            style_losses.append(style_loss)
    # now we trim off the layers after the last content and style losses
    for i in range(len(model) - 1, -1, -1):
        if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
            break
    # FAQ:反向修正模型
    model = model[:(i + 1)]
    return model, style_losses, content_losses


input_img = content_img.clone()


# if you want to use white noise instead uncomment the below line:
# input_img = torch.randn(content_img.data.size(), device=device)
# add the original input image to the figure:
# plt.figure()
# imshow(input_img, title='Input Image')
def get_input_optimizer(input_img):
    # this line to show that input is a parameter that requires a gradient
    optimizer = optim.LBFGS([input_img.requires_grad_()])
    return optimizer


def run_style_transfer(cnn, normalization_mean, normalization_std,
                       content_img, style_img, input_img, num_steps=300,
                       style_weight=1000000, content_weight=1):
    """Run the style transfer."""
    print('Building the style transfer model..')
    model, style_losses, content_losses = get_style_model_and_losses(cnn,
                                                                     normalization_mean, normalization_std,
                                                                     style_img,content_img,)
    optimizer = get_input_optimizer(input_img)
    print('model',model)
    print('Optimizing..')
    run = [0]
    while run[0] <= num_steps:
        def closure():
            # REW:input_img 是需要更新的 很特别
            input_img.data.clamp_(0, 1)
            optimizer.zero_grad()
            model(input_img)
            style_score = 0
            content_score = 0
            for sl in style_losses:  # FAQ:REW:这也能自动变 当容器看
                style_score += sl.loss  # 每个都是变量
            for cl in content_losses:
                content_score += cl.loss
            style_score *= style_weight
            content_score *= content_weight

            loss = style_score + content_score
            loss.backward()
            run[0] += 1
            if run[0] % 50 == 0:
                print("run {}:".format(run))
                print('Style Loss : {:4f} Content Loss: {:4f}'.format(
                    style_score.item(), content_score.item()))
                print()
            return style_score + content_score

        optimizer.step(closure)
    input_img.data.clamp_(0, 1)
    return input_img


output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
                            content_img, style_img, input_img)

plt.figure()
imshow(output, title='Output Image')

# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
